{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:05:34.773250500Z",
     "start_time": "2024-05-02T12:05:29.764027200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=9, micro=7, releaselevel='final', serial=0)\n",
      "matplotlib 3.8.4\n",
      "numpy 1.26.4\n",
      "pandas 2.2.2\n",
      "sklearn 1.4.2\n",
      "torch 2.2.2+cpu\n",
      "cpu\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "    \n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42\n",
    "torch.manual_seed(seed)\n",
    "torch.cuda.manual_seed_all(seed)\n",
    "np.random.seed(seed)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:06:27.584572500Z",
     "start_time": "2024-05-02T12:06:27.140763300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "May I borrow this book?\n",
      "¿Puedo tomar prestado este libro?\n",
      "may i borrow this book ?\n",
      "¿ puedo tomar prestado este libro ?\n",
      "b'\\xc2\\xbf puedo tomar prestado este libro ?'\n"
     ]
    }
   ],
   "source": [
    "import unicodedata\n",
    "import re\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#因为西班牙语有一些是特殊字符，所以我们需要unicode转ascii，\n",
    "# 这样值变小了，因为unicode太大\n",
    "def unicode_to_ascii(s):\n",
    "    #NFD是转换方法，把每一个字节拆开，Mn是重音，所以去除\n",
    "    return ''.join(c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn')\n",
    "\n",
    "#下面我们找个样本测试一下\n",
    "# 加u代表对字符串进行unicode编码\n",
    "en_sentence = u\"May I borrow this book?\"\n",
    "sp_sentence = u\"¿Puedo tomar prestado este libro?\"\n",
    "\n",
    "print(unicode_to_ascii(en_sentence))\n",
    "print(unicode_to_ascii(sp_sentence))\n",
    "\n",
    "\n",
    "def preprocess_sentence(w):\n",
    "    #变为小写，去掉多余的空格\n",
    "    w = unicode_to_ascii(w.lower().strip())\n",
    "\n",
    "    # 在单词与跟在其后的标点符号之间插入一个空格\n",
    "    # eg: \"he is a boy.\" => \"he is a boy .\"\n",
    "    # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation\n",
    "    w = re.sub(r\"([?.!,¿])\", r\" \\1 \", w)\n",
    "    #因为可能有多余空格，替换为一个空格，所以处理一下\n",
    "    w = re.sub(r'[\" \"]+', \" \", w)\n",
    "\n",
    "    # 除了 (a-z, A-Z, \".\", \"?\", \"!\", \",\")，将所有字符替换为空格\n",
    "    w = re.sub(r\"[^a-zA-Z?.!,¿]+\", \" \", w)\n",
    "\n",
    "    w = w.rstrip().strip()\n",
    "\n",
    "    return w\n",
    "\n",
    "print(preprocess_sentence(en_sentence))\n",
    "print(preprocess_sentence(sp_sentence))\n",
    "print(preprocess_sentence(sp_sentence).encode('utf-8'))  #¿是占用两个字节的"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "array([False,  True,  True, ...,  True,  True,  True])"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_index = np.random.choice(a=[\"train\", \"test\"], replace=True, p=[0.9, 0.1], size=118964)\n",
    "split_index=='train'"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-02T12:10:36.913241100Z",
     "start_time": "2024-05-02T12:10:36.885122800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:13:15.140680800Z",
     "start_time": "2024-05-02T12:13:08.375691600Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    fpath = Path(r\"./data_spa_en/spa.txt\")\n",
    "    cache_path = Path(r\"./.cache/lang_pair.npy\")\n",
    "    split_index = np.random.choice(a=[\"train\", \"test\"], replace=True, p=[0.9, 0.1], size=118964) #按照9:1划分训练集和测试集\n",
    "    def __init__(self, mode=\"train\", cache=False):\n",
    "        if cache or not self.cache_path.exists():\n",
    "            self.cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            with open(self.fpath, \"r\", encoding=\"utf8\") as file:\n",
    "                lines = file.readlines()\n",
    "                lang_pair = [[preprocess_sentence(w) for w in l.split('\\t')]  for l in lines]\n",
    "                trg, src = zip(*lang_pair)\n",
    "                trg=np.array(trg)\n",
    "                src=np.array(src)\n",
    "                np.save(self.cache_path, {\"trg\": trg, \"src\": src}) #保存为npy文件,方便下次直接读取,不用再处理\n",
    "        else:\n",
    "            lang_pair = np.load(self.cache_path, allow_pickle=True).item() #读取npy文件，allow_pickle=True允许读取字典\n",
    "            trg = lang_pair[\"trg\"]\n",
    "            src = lang_pair[\"src\"]\n",
    "        \n",
    "        self.trg = trg[self.split_index == mode] #按照index拿到训练集的标签\n",
    "        self.src = src[self.split_index == mode] #按照index拿到训练集的源语言\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index] #返回一个样本的源语言和目标语言\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "        \n",
    "\n",
    "train_ds = LangPairDataset(\"train\")\n",
    "test_ds = LangPairDataset(\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:13:50.185560Z",
     "start_time": "2024-05-02T12:13:50.170553600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source: si quieres sonar como un hablante nativo , debes estar dispuesto a practicar diciendo la misma frase una y otra vez de la misma manera en que un musico de banjo practica el mismo fraseo una y otra vez hasta que lo puedan tocar correctamente y en el tiempo esperado .\n",
      "target: if you want to sound like a native speaker , you must be willing to practice saying the same sentence over and over in the same way that banjo players practice the same phrase over and over until they can play it correctly and at the desired tempo .\n"
     ]
    }
   ],
   "source": [
    "print(\"source: {}\\ntarget: {}\".format(*train_ds[-1]))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现不共享的方案。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:13:57.547526400Z",
     "start_time": "2024-05-02T12:13:56.089537800Z"
    }
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "def get_word_idx(ds, mode=\"src\", threshold=2):\n",
    "    #载入词表，看下词表长度，词表就像英语字典\n",
    "    word2idx = {\n",
    "        \"[PAD]\": 0,     # 填充 token\n",
    "        \"[BOS]\": 1,     # begin of sentence\n",
    "        \"[UNK]\": 2,     # 未知 token\n",
    "        \"[EOS]\": 3,     # end of sentence\n",
    "    }\n",
    "    idx2word = {value: key for key, value in word2idx.items()}\n",
    "    index = len(idx2word)\n",
    "    threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "    word_list = \" \".join([pair[0 if mode==\"src\" else 1] for pair in ds]).split()\n",
    "    counter = Counter(word_list) #统计词频\n",
    "\n",
    "    for token, count in counter.items():\n",
    "        if count >= threshold:#出现次数大于阈值的token加入词表\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "                \n",
    "    return word2idx, idx2word\n",
    "\n",
    "src_word2idx, src_idx2word = get_word_idx(train_ds, \"src\") #源语言词表\n",
    "trg_word2idx, trg_idx2word = get_word_idx(train_ds, \"trg\") #目标语言词表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:15:39.260418700Z",
     "start_time": "2024-05-02T12:15:39.210447Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw text----------\n",
      "['hello', 'world']\n",
      "['tokenize', 'text', 'datas', 'with', 'batch']\n",
      "['this', 'is', 'a', 'test']\n",
      "mask----------\n",
      "indices----------\n",
      "tensor([  17, 3228,    3,    0,    0,    0])\n",
      "tensor([   2, 3872,    2,  545,    2,    3])\n",
      "tensor([ 121,  236,  107, 1265,    3,    0])\n",
      "decode text----------\n",
      "hello world [EOS] [PAD] [PAD] [PAD]\n",
      "[UNK] text [UNK] with [UNK] [EOS]\n",
      "this is a test [EOS] [PAD]\n"
     ]
    }
   ],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=500, pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "    \n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True, return_mask=False):\n",
    "        \"\"\"如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token\n",
    "        \"\"\"\n",
    "        max_length = min(self.max_length, add_eos + add_bos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_bos - add_eos]] #如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list) #转换为tensor\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.int64) #mask是一个和input_ids一样大小的tensor，里面的值是0或者1，0代表token，1代表padding\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "    \n",
    "    \n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":\n",
    "                    break\n",
    "                text.append(word)\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "    \n",
    "\n",
    "src_tokenizer = Tokenizer(word2idx=src_word2idx, idx2word=src_idx2word)\n",
    "trg_tokenizer = Tokenizer(word2idx=trg_word2idx, idx2word=trg_idx2word)\n",
    "\n",
    "trg_tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False,return_mask=True)\n",
    "raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "indices= trg_tokenizer.encode(raw_text, padding_first=False, add_bos=False, add_eos=True)\n",
    "decode_text = trg_tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "print(\"raw text\"+'-'*10)\n",
    "for raw in raw_text:\n",
    "    print(raw)\n",
    "print(\"mask\"+'-'*10)\n",
    "# for m in mask:\n",
    "#     print(m)\n",
    "print(\"indices\"+'-'*10)\n",
    "for index in indices:\n",
    "    print(index)\n",
    "print(\"decode text\"+'-'*10)\n",
    "for decode in decode_text:\n",
    "    print(decode)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def collate_fct(batch):\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "    \n",
    "    # [PAD] [BOS] src [EOS]\n",
    "    encoder_inputs, encoder_inputs_mask = src_tokenizer.encode(\n",
    "        src_words, padding_first=True, add_bos=True, add_eos=True, return_mask=True\n",
    "        )\n",
    "    \n",
    "    # [BOS] trg [PAD]\n",
    "    decoder_inputs = trg_tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False,\n",
    "        )\n",
    "    \n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = trg_tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "        )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device=device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device=device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device=device),\n",
    "        \"decoder_labels\": decoder_labels.to(device=device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device=device),\n",
    "    }\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "encoder_inputs\n",
      "tensor([[   0,    0,    0,    0,    1,  350, 4002, 2826, 2827,    5,    3],\n",
      "        [   1,   12, 2266,  706,   80,  294,   88,   83,  297,   14,    3]],\n",
      "       device='cuda:0')\n",
      "encoder_inputs_mask\n",
      "tensor([[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\n",
      "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], device='cuda:0')\n",
      "decoder_inputs\n",
      "tensor([[   1,  197,  756, 1413,    5,    0,    0,    0,    0,    0],\n",
      "        [   1,  332,   90, 1090,  443,  158,   31,  680, 3688,   10]],\n",
      "       device='cuda:0')\n",
      "decoder_labels\n",
      "tensor([[ 197,  756, 1413,    5,    3,    0,    0,    0,    0,    0],\n",
      "        [ 332,   90, 1090,  443,  158,   31,  680, 3688,   10,    3]],\n",
      "       device='cuda:0')\n",
      "decoder_labels_mask\n",
      "tensor([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
      "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "sample_dl = DataLoader(train_ds, batch_size=2, shuffle=True, collate_fn=collate_fct)\n",
    "\n",
    "for batch in sample_dl:\n",
    "    for key, value in batch.items():\n",
    "        print(key)\n",
    "        print(value)\n",
    "    break"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    def __init__(\n",
    "        self, \n",
    "        vocab_size, \n",
    "        embedding_dim=256, \n",
    "        hidden_dim=1024, \n",
    "        num_layers=1,\n",
    "        ):\n",
    "        super().__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
    "        self.gru = nn.GRU(embedding_dim, hidden_dim, num_layers=num_layers, batch_first=True)\n",
    "        \n",
    "    def forward(self, encoder_inputs):\n",
    "        # encoder_inputs.shape = [batch size, sequence length]\n",
    "        bs, seq_len = encoder_inputs.shape\n",
    "        embeds = self.embedding(encoder_inputs)\n",
    "        # embeds.shape = [batch size, sequence length, embedding_dim]\n",
    "        seq_output, hidden = self.gru(embeds)\n",
    "        # seq_output.shape = [batch size, sequence length, hidden_dim]\n",
    "        return seq_output, hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BahdanauAttention(nn.Module):\n",
    "    def __init__(self, hidden_dim=1024):\n",
    "        super().__init__()\n",
    "        self.Wk = nn.Linear(hidden_dim, hidden_dim)\n",
    "        self.Wq = nn.Linear(hidden_dim, hidden_dim)\n",
    "        self.V = nn.Linear(hidden_dim, 1)\n",
    "        \n",
    "    def forward(self, query, keys, values, attn_mask=None):\n",
    "        # query.shape = [batch size, hidden_dim]\n",
    "        # keys.shape = [batch size, sequence length, hidden_dim]\n",
    "        # values.shape = [batch size, sequence length, hidden_dim]\n",
    "        # attention_mask.shape = [batch size, sequence length]\n",
    "        scores = self.V(F.tanh(self.Wk(keys) + self.Wq(query.unsqueeze(-2))))\n",
    "        # score.shape = [batch size, sequence length, 1]\n",
    "        if attn_mask is not None:\n",
    "            # attn_mask is a matrix of 0/1 element,\n",
    "            # 1 means to mask logits while 0 means do nothing\n",
    "            # here we add -inf to the element while mask == 1\n",
    "            attn_mask = (attn_mask.unsqueeze(-1)) * -1e16\n",
    "            scores += attn_mask\n",
    "        scores = F.softmax(scores, dim=-2)\n",
    "        # score.shape = [batch size, sequence length, 1]\n",
    "        context_vector = torch.mul(scores, values).sum(dim=-2)\n",
    "        # context_vector.shape = [batch size, hidden_dim]\n",
    "        return context_vector, scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Decoder(nn.Module):\n",
    "    def __init__(\n",
    "        self, \n",
    "        vocab_size, \n",
    "        embedding_dim=256, \n",
    "        hidden_dim=1024, \n",
    "        num_layers=1,\n",
    "        ):\n",
    "        super(Decoder, self).__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
    "        self.gru = nn.GRU(embedding_dim + hidden_dim, hidden_dim, num_layers=num_layers, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_dim, vocab_size)\n",
    "        self.dropout = nn.Dropout(0.6)\n",
    "        self.attention = BahdanauAttention(hidden_dim)\n",
    "        \n",
    "    def forward(self, decoder_input, hidden, encoder_outputs, attn_mask=None):\n",
    "        # decoder_input.shape = [batch size, 1]\n",
    "        assert len(decoder_input.shape) == 2 and decoder_input.shape[-1] == 1, f\"decoder_input.shape = {decoder_input.shape}\"\n",
    "        # hidden.shape = [batch size, hidden_dim]\n",
    "        assert len(hidden.shape) == 2, f\"hidden.shape = {hidden.shape}\"\n",
    "        # encoder_outputs.shape = [batch size, sequence length, hidden_dim]\n",
    "        assert len(encoder_outputs.shape) == 3, f\"encoder_outputs.shape = {encoder_outputs.shape}\"\n",
    "        \n",
    "        context_vector, attention_score = self.attention(\n",
    "            query=hidden, keys=encoder_outputs, values=encoder_outputs, attn_mask=attn_mask)\n",
    "        # context_vector.shape = [batch size, hidden_dim]\n",
    "        embeds = self.embedding(decoder_input)\n",
    "        # embeds.shape = [batch size, 1, embedding_dim]\n",
    "        # concatenate\n",
    "        embeds = torch.cat([context_vector.unsqueeze(-2), embeds], dim=-1)\n",
    "        # embeds.shape = [batch size, 1, embedding_dim + hidden_dim]\n",
    "        seq_output, hidden = self.gru(embeds)\n",
    "        # seq_output.shape = [batch size, 1, hidden_dim]\n",
    "        logits = self.fc(self.dropout(seq_output))\n",
    "        # logits.shape = [batch size, 1, vocab size]\n",
    "        return logits, hidden, attention_score\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Sequence2Sequence(nn.Module):\n",
    "    def __init__(\n",
    "        self, \n",
    "        src_vocab_size, #输入词典大小\n",
    "        trg_vocab_size, #输出词典大小\n",
    "        encoder_embedding_dim=256, \n",
    "        encoder_hidden_dim=1024, \n",
    "        encoder_num_layers=1,\n",
    "        decoder_embedding_dim=256, \n",
    "        decoder_hidden_dim=1024, \n",
    "        decoder_num_layers=1,\n",
    "        bos_idx=1,\n",
    "        eos_idx=3,\n",
    "        max_length=512,\n",
    "        ):\n",
    "        super(Sequence2Sequence, self).__init__()\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.max_length = max_length\n",
    "        self.encoder = Encoder(\n",
    "            src_vocab_size, \n",
    "            embedding_dim=encoder_embedding_dim, \n",
    "            hidden_dim=encoder_hidden_dim,\n",
    "            num_layers=encoder_num_layers,\n",
    "            )\n",
    "        self.decoder = Decoder(\n",
    "            trg_vocab_size, \n",
    "            embedding_dim=decoder_embedding_dim, \n",
    "            hidden_dim=decoder_hidden_dim,\n",
    "            num_layers=decoder_num_layers,\n",
    "            )\n",
    "        \n",
    "    def forward(self, *, encoder_inputs, decoder_inputs, attn_mask=None):\n",
    "        # encoding\n",
    "        encoder_outputs, hidden = self.encoder(encoder_inputs)\n",
    "        # decoding with teacher forcing\n",
    "        bs, seq_len = decoder_inputs.shape\n",
    "        logits_list = []\n",
    "        scores_list = []\n",
    "        for i in range(seq_len):\n",
    "            # 每次迭代生成一个时间步的预测，存储在 logits_list 中，并且记录注意力分数（如果有的话）在 scores_list 中，最后将预测的logits和注意力分数拼接并返回。\n",
    "            logits, hidden, score = self.decoder(\n",
    "                decoder_inputs[:, i:i+1], \n",
    "                hidden[-1], # the hidden state of the last layer\n",
    "                encoder_outputs, \n",
    "                attn_mask=attn_mask\n",
    "                )\n",
    "            logits_list.append(logits)\n",
    "            scores_list.append(score)\n",
    "        \n",
    "        return torch.cat(logits_list, dim=-2), torch.cat(scores_list, dim=-1)\n",
    "    \n",
    "    @torch.no_grad()\n",
    "    def infer(self, encoder_input, attn_mask=None):\n",
    "        #infer用于预测\n",
    "        # encoder_input.shape = [1, sequence length]\n",
    "        # encoding\n",
    "        encoder_outputs, hidden = self.encoder(encoder_input)\n",
    "        \n",
    "        # decoding\n",
    "        decoder_input = torch.Tensor([self.bos_idx]).reshape(1, 1).to(dtype=torch.int64) #shape为[1,1]，内容为开始标记\n",
    "        decoder_pred = None\n",
    "        pred_list = []\n",
    "        score_list = []\n",
    "        # 从开始标记 bos_idx 开始，迭代地生成序列，直到生成结束标记 eos_idx 或达到最大长度 max_length。\n",
    "        for _ in range(self.max_length):\n",
    "            logits, hidden, score = self.decoder(\n",
    "                decoder_input, \n",
    "                hidden[-1], \n",
    "                encoder_outputs, \n",
    "                attn_mask=attn_mask\n",
    "                )\n",
    "            # using greedy search\n",
    "            decoder_pred = logits.argmax(dim=-1)\n",
    "            decoder_input = decoder_pred\n",
    "            pred_list.append(decoder_pred.reshape(-1).item())\n",
    "            score_list.append(score)\n",
    "            \n",
    "            # stop at eos token\n",
    "            if decoder_pred == self.eos_idx:\n",
    "                break\n",
    "            \n",
    "        # return\n",
    "        return pred_list, torch.cat(score_list, dim=-1)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_entropy_with_padding(logits, labels, padding_mask=None):\n",
    "    # logits.shape = [batch size, sequence length, num of classes]\n",
    "    # labels.shape = [batch size, sequence length]\n",
    "    # padding_mask.shape = [batch size, sequence length]\n",
    "    bs, seq_len, nc = logits.shape\n",
    "    loss = F.cross_entropy(logits.reshape(bs * seq_len, nc), labels.reshape(-1), reduce=False)\n",
    "    if padding_mask is None:#如果没有padding_mask，就直接求平均\n",
    "        loss = loss.mean()\n",
    "    else:\n",
    "        # 如果提供了 padding_mask，则将填充部分的损失去除后计算有效损失的均值。首先，通过将 padding_mask reshape 成一维张量，并取 1 减去得到填充掩码。这样填充部分的掩码值变为 1，非填充部分变为 0。将损失张量与填充掩码相乘，这样填充部分的损失就会变为 0。然后，计算非填充部分的损失和（sum）以及非填充部分的掩码数量（sum）作为有效损失的均值计算。(因为上面我们设计的mask的token是0，所以这里是1-padding_mask)\n",
    "        padding_mask = 1 - padding_mask.reshape(-1)\n",
    "        loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "    return loss\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Callback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "\n",
    "class TensorBoardCallback:\n",
    "    def __init__(self, log_dir, flush_secs=10):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            log_dir (str): dir to write log.\n",
    "            flush_secs (int, optional): write to dsk each flush_secs seconds. Defaults to 10.\n",
    "        \"\"\"\n",
    "        self.writer = SummaryWriter(log_dir=log_dir, flush_secs=flush_secs)\n",
    "\n",
    "    def draw_model(self, model, input_shape):\n",
    "        self.writer.add_graph(model, input_to_model=torch.randn(input_shape))\n",
    "        \n",
    "    def add_loss_scalars(self, step, loss, val_loss):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/loss\", \n",
    "            tag_scalar_dict={\"loss\": loss, \"val_loss\": val_loss},\n",
    "            global_step=step,\n",
    "            )\n",
    "        \n",
    "    def add_acc_scalars(self, step, acc, val_acc):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/accuracy\",\n",
    "            tag_scalar_dict={\"accuracy\": acc, \"val_accuracy\": val_acc},\n",
    "            global_step=step,\n",
    "        )\n",
    "        \n",
    "    def add_lr_scalars(self, step, learning_rate):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/learning_rate\",\n",
    "            tag_scalar_dict={\"learning_rate\": learning_rate},\n",
    "            global_step=step,\n",
    "            \n",
    "        )\n",
    "    \n",
    "    def __call__(self, step, **kwargs):\n",
    "        # add loss\n",
    "        loss = kwargs.pop(\"loss\", None)\n",
    "        val_loss = kwargs.pop(\"val_loss\", None)\n",
    "        if loss is not None and val_loss is not None:\n",
    "            self.add_loss_scalars(step, loss, val_loss)\n",
    "        # add acc\n",
    "        acc = kwargs.pop(\"acc\", None)\n",
    "        val_acc = kwargs.pop(\"val_acc\", None)\n",
    "        if acc is not None and val_acc is not None:\n",
    "            self.add_acc_scalars(step, acc, val_acc)\n",
    "        # add lr\n",
    "        learning_rate = kwargs.pop(\"lr\", None)\n",
    "        if learning_rate is not None:\n",
    "            self.add_lr_scalars(step, learning_rate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        \"\"\"\n",
    "        Save checkpoints each save_epoch epoch. \n",
    "        We save checkpoint by epoch in this implementation.\n",
    "        Usually, training scripts with pytorch evaluating model and save checkpoint by step.\n",
    "\n",
    "        Args:\n",
    "            save_dir (str): dir to save checkpoint\n",
    "            save_epoch (int, optional): the frequency to save checkpoint. Defaults to 1.\n",
    "            save_best_only (bool, optional): If True, only save the best model or save each model at every epoch.\n",
    "        \"\"\"\n",
    "        self.save_dir = save_dir\n",
    "        self.save_step = save_step\n",
    "        self.save_best_only = save_best_only\n",
    "        self.best_metrics = - np.inf\n",
    "        \n",
    "        # mkdir\n",
    "        if not os.path.exists(self.save_dir):\n",
    "            os.mkdir(self.save_dir)\n",
    "        \n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return\n",
    "        \n",
    "        if self.save_best_only:\n",
    "            assert metric is not None\n",
    "            if metric >= self.best_metrics:\n",
    "                # save checkpoints\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"best.ckpt\"))\n",
    "                # update best metrics\n",
    "                self.best_metrics = metric\n",
    "        else:\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            patience (int, optional): Number of epochs with no improvement after which training will be stopped.. Defaults to 5.\n",
    "            min_delta (float, optional): Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute \n",
    "                change of less than min_delta, will count as no improvement. Defaults to 0.01.\n",
    "        \"\"\"\n",
    "        self.patience = patience\n",
    "        self.min_delta = min_delta\n",
    "        self.best_metric = - np.inf\n",
    "        self.counter = 0\n",
    "        \n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:\n",
    "            # update best metric\n",
    "            self.best_metric = metric\n",
    "            # reset counter \n",
    "            self.counter = 0\n",
    "        else: \n",
    "            self.counter += 1\n",
    "            \n",
    "    @property\n",
    "    def early_stop(self):\n",
    "        return self.counter >= self.patience\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def evaluating(model, dataloader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in dataloader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "        \n",
    "        # 前向计算\n",
    "        logits, _ = model(\n",
    "            encoder_inputs=encoder_inputs, \n",
    "            decoder_inputs=decoder_inputs, \n",
    "            attn_mask=encoder_inputs_mask\n",
    "            )\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)         # 验证集损失\n",
    "        loss_list.append(loss.cpu().item())\n",
    "        \n",
    "    return np.mean(loss_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-05 01:37:20.137532: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "21d5df412af1413e82ff5059107264b6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/33520 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ouyanghongyu/anaconda3/envs/pytorch/lib/python3.10/site-packages/torch/nn/functional.py:1956: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n",
      "/home/ouyanghongyu/anaconda3/envs/pytorch/lib/python3.10/site-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.\n",
      "  warnings.warn(warning.format(ret))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 4 / global_step 7600\n"
     ]
    }
   ],
   "source": [
    "# 训练\n",
    "def training(\n",
    "    model, \n",
    "    train_loader, \n",
    "    val_loader, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    tensorboard_callback=None,\n",
    "    save_ckpt_callback=None,\n",
    "    early_stop_callback=None,\n",
    "    eval_step=500,\n",
    "    ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "    \n",
    "    global_step = 1\n",
    "    model.train()\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # training\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "                \n",
    "                # 梯度清空\n",
    "                optimizer.zero_grad()\n",
    "                \n",
    "                # 前向计算\n",
    "                logits, _ = model(\n",
    "                    encoder_inputs=encoder_inputs, \n",
    "                    decoder_inputs=decoder_inputs, \n",
    "                    attn_mask=encoder_inputs_mask\n",
    "                    )\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "                \n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "                \n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "            \n",
    "                loss = loss.cpu().item()\n",
    "                # record\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss, \"step\": global_step\n",
    "                })\n",
    "                \n",
    "                # evaluating\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss, \"step\": global_step\n",
    "                    })\n",
    "                    model.train()\n",
    "                    \n",
    "                    # 1. 使用 tensorboard 可视化\n",
    "                    if tensorboard_callback is not None:\n",
    "                        tensorboard_callback(\n",
    "                            global_step, \n",
    "                            loss=loss, val_loss=val_loss,\n",
    "                            lr=optimizer.param_groups[0][\"lr\"],\n",
    "                            )\n",
    "                \n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), metric=-val_loss)\n",
    "\n",
    "                    # 3. 早停 Early Stop\n",
    "                    if early_stop_callback is not None:\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict\n",
    "                    \n",
    "                # udate step\n",
    "                global_step += 1\n",
    "                pbar.update(1)\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss})\n",
    "        \n",
    "    return record_dict\n",
    "        \n",
    "\n",
    "epoch = 20\n",
    "batch_size = 64\n",
    "\n",
    "model = Sequence2Sequence(src_vocab_size=len(src_word2idx), trg_vocab_size=len(trg_word2idx))\n",
    "train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True, collate_fn=collate_fct)\n",
    "test_dl = DataLoader(test_ds, batch_size=batch_size, shuffle=False, collate_fn=collate_fct)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = cross_entropy_with_padding\n",
    "# 2. 定义优化器 采用 adam\n",
    "# Optimizers specified in the torch.optim package\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 1. tensorboard 可视化\n",
    "if not os.path.exists(\"runs\"):\n",
    "    os.mkdir(\"runs\")\n",
    "exp_name = \"translate-seq2seq\"\n",
    "tensorboard_callback = TensorBoardCallback(f\"runs/{exp_name}\")\n",
    "# tensorboard_callback.draw_model(model, [1, MAX_LENGTH])\n",
    "# 2. save best\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(\n",
    "    f\"checkpoints/{exp_name}\", save_step=200, save_best_only=True)\n",
    "# 3. early stop\n",
    "early_stop_callback = EarlyStopCallback(patience=5)\n",
    "\n",
    "model = model.to(device)\n",
    "\n",
    "record = training(\n",
    "    model, \n",
    "    train_dl, \n",
    "    test_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    tensorboard_callback=tensorboard_callback,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=200\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k5goiIiJZKDJYlN+IjIiIiGqHIoNFQkSgy+OPfr8Vu09m1W5hiIiIfIgigwUAPDm4ldOxpXvP4oaP12H/mRwZSkRERKR8ig0WYQHaCs9tP36pFktCRETkOxQbLIiIiKj2KTZYCCHkLgIREZHPUWyw6NeykdxFICIi8jmKDRYtIoMqPPfR8kPYnH6xFktDRETkGxQbLADgwOvXuDx+NqcYt3++AXd9sRFbjzJgEBEReYqig4WfVl3p+Q1pF3DrZxtqqTRERETKp+hgAQDNG7leLIuIiIg8T/HBYki7aLmLQERE5DMUHyy4IRkREVHtUXywUDFYEBER1ZrLChbTpk2DJEmYMGGCh4rjeaN6x1d5zbAP1uBwZm4tlIaIiEjZahwstmzZgs8//xydOnXyZHk8rnGYf5XXpJ7NxYSfdnq/MERERApXo2CRl5eHUaNG4csvv0SDBg08XSZZ5BUZUWw04eXfU7B8/1m5i0NERFQvaWrypDFjxmDEiBEYPHgw3njjjUqvLS4uRnFxse1xTo5ly3KDwQCDwVCTl3fJeq+a3lMIYM7aNHy/8Ri+33gMh14f6rGyedrl1rW+8aX6sq7K5Uv1ZV2Vqbp1lISbu3XNnz8fb775JrZs2QI/Pz9cddVV6NKlC2bMmOHy+smTJ2PKlClOx+fOnYuAgAB3XrrGxm+oOj9F6AXaNxRYfcbSiPNhktHbxSIiIqo3CgoKcPfddyM7OxshISEVXudWi8WJEycwfvx4JCcnw8/Pr1rPmTRpEiZOnGh7nJOTg9jYWAwdOrTSgrnLYDAgOTkZQ4YMgVardTg3fsO/VT7fqNIhMSEGq88cBwAMHz7cY2XztMrqqkS+VF/WVbl8qb6sqzJZexyq4law2LZtGzIzM9GtWzfbMZPJhDVr1uDjjz9GcXEx1GrHZbT1ej30er3TvbRarVfehJreN6vQ4FD2+vAPxFt/h3WVL9WXdVUuX6ov66os1a2fW8Fi0KBB2LNnj8OxBx54AG3atMFzzz3nFCrqsxKjGTqN4pf5ICIi8ii3gkVwcDA6dOjgcCwwMBDh4eFOx+ujr9em275fmZqJYe25HDgREZE7+Ct5Bcxmt8a0EhEREWo43dTeqlWrPFAM72oTHYwDGVxZk4iIyNt8osVizgO9MO7qFnIXg4iISPF8IlhEh/ph4tDWbj2Hu6ISERG5zyeChdW8h/tU+9rHftgOg8nsxdIQEREpj08Fi6Tm4W5dv3x/ppdKQkREpEw+FSwA4Mv7elT72mKjyYslISIiUh6fCxZD2kW5/ZyDZ3OxdG+GF0pDRESkLD4XLABABTNuVK3Fo+q/Kr1OCOCvXacx9IM1ePT7bdhy9GItlZCIiKh+uux1LOqjHlIqPtR9imKhxR+mvsiA67EXE37a6fD4t20n0TOhYS2UkIiIqH7yyRaLzaINNpnbQC8Z8Ljmz2o/b/6WEwCA7AIDnv5lF9YfOe+tIhIREdVLPhksvrqvJ2Zr7wIA3KlegcaofkAwmsyYumQ/ft12End/uclbRSQiIqqXfDJYDG4Xhc9enoCTYT2gk0wYo/mj2s+dueIwjl8s8GLpiIiI6i+fDBZWTW96HQBwu3oVmkrnqvWcD5cf8mKJiIiI6jefDhaI7wtzs4HQSiaMVS+s9tPWH7ngxUIRERHVX74dLACoBr4AALhVvQZx0lmZS0NERFS/+XywQGwvrDR1hkYyY5ym+q0WRERE5IzBAsCxTuMAADep/kOidMat5+YUGbxRJCIionqJwQLA/bfdimWmrlBLAk+42Wpx48frIITAzOWHsCqVm5YREZFvY7AoNTdgFADgRtU6NJdOVft56efzsWx/Jt5PPoj7Z2/xVvGIiIjqBQaLUu+NG42lph5QSwLjNQvceu6SFPe6T4iIiJSKwaJUw0AdFobeCwC4TrURraQT1X5u8l7OJiEiIgIYLBx8MnE0Fpl6QSUJjNf8Vu3n5RYbvVgqIiKi+oPBwo5aJSGvz9MwCwkj1JvRRjru9j0e+nYL5m22PO/g2Vz8k5Lh6WISERHVWQwW5XTo2geLzL0BABPcaLWwWrY/E5MW7MGkBXsw9IM1eOyHbdhy9KKni0lERFQnMViUI0HCDOMtMAsJ16i3oL2UXqP7WFstAOBARq6nikdERFSnMViU0zwyEEdEE/xh7gugZq0W5amky74FERFRvcBgUY5eo8aITjH4yHgzTELCEPV2dJKOXNY9DUYzZq9Lxy9bqz/ThIiIqD5isHCha2wY0kUMFpr7A7j8Vos3F+/HlL/24Zlfd0MI4YkiEhER1UkMFi6M7psAAPjIeBOMQoWr1TvRVTpU4/sZTGVhQgjgdFYhth27xJBBRESKo5G7AHWRVm3JW8dFFH4zXYk7NKvwpOZX3GeYdNn3FgD6Tlthe9w6KhhLn7zysu9LRERUF7DFogozTSNhEGpcqd6DHtKBy77fhiMXHB6nni2bMXIpvwSPfLcVy/ZxJU8iIqqfGCyqcFJE4kyzWwAAk7TzoIbpsu53z9ebKjw3bckB/LvvLB76bisAYNeJLNz1xUaknMq+rNckIiKqLQwW1RA38lWYtYHorjqE5zTzvfY6mblFDo9HfroOG9Iu4K4vNnrtNYmIiDyJwaICvzyWhHYxIfj1sSQgtClUN80CADyiWYQRKs9+0OeX7jVSfiindWwn9yIhIqL6gsGiAj0TGmLx+P7okdDQcqDdjcAV4wEA72g/R0vppMde69dtlnvZTxI5cbGgyucJIfDy7yn4ck2ax8pCRER0ORgs3HH1K0DiAARKxfgh6EMEo+oP/+o4eiEf6efzYTKXJYt3l6ZW+bxdJ7Px/cZjeHPxfo+Ug4iI6HIxWLhDrQFu/QYIaYoowylM186CBPNl33b2uqMY+N4qrD183nZsa7mNy5btO4ur31uF7ccv2Y7ll1zeQFIiIiJPY7BwV2AEcMf3MKv1GKLehsfVf3rlZU5nOw7kfOi7rUg7n4+bP10Pk1ngVD4wazW7QIiIqG5hsKiJJt2gGvEeAOApzS8YoNpVqy//7YZjeGe3BpvSL1V9MRERUS1isKipbvdhrvFqqCSBD7Ufo6mUWWsvPfWfg7X2WkRERO5gsLgMxmHTsNPcHGFSPj7XfgA/FMtdJCIiIlkxWFyG2/u0wHPqp3FehKC96hje1H4N59UoapcnNjY7eDYXBSVcO4OIiNzHYHEZ/LRqLH31bkTc/yOEpMIt6rW4V50sS1mEEPhh4zEkTlqM/w6dgxACczcdd3s58HWHz2PoB2tw7Yf/uXyNMXO34+lfandMCRER1R8MFp6QeCUu9n0JAPCK5nt0k2p/DMTqg+fw0u8pAIB7v96MpXvP4oWFe3DdzLUwmsw4lVVYrfv8tes0AODYBec1Ok5nF2HR7jP4ddtJtmgQEZFLDBYeEj54IlZqroBWMmGWbgYaIatWX3/nCcfXe/bXslaFu7/ahCumrcDkP/dWeg8hBOZvOeHy3CcrD+OGmWvtrq15WYmISLkYLDxFktBk9Dc4aG6CKCkLn+g+hAa191v9gu2nHB7nFJW99uZ0y2Jbc9YfxcY0x23b7a04UPHMlneXpuJCfslllpKIiJSOwcKDWsVG4/DAz5Ej/NFLlYpp2q8ue5v16jpejb1FAEvLRmZuESb+tBPbjjmug3EhzzE4rD98HrtPZrm8DxssiIjIFQYLDxs+sD8mGh6HSUi4Vb0GX2inwx9FVT+xlhhNZrywYA8W7DiFW2atr/Tau7/ahBs+XufyXE6hwRvFIyKieo7BwguWmbvjMcOTKBJaDFLvwDzdGwiHe7MzvKWgxIS08/m2x6ezCpF+Ph//pJzBs7/trvZ9+k5bgVf+SMHhzDxvFJOIiOopBgsvSTb3wN0lL+KiCEIXVRp+001GvJQhd7Hw6aojSDtXFiz6TluBge+twmM/bHf7Xt9tOIbB01d7ZO0MIiJSBgYLLxg7sAUAYLtohVtLJuO8JhoJqrP4TTcZnaQjMpfO8+76ciNu/2wD5m8+LndRiIhIZgwWXvD0sNbYPXkoACBNNMbcjl8jxZyACCkH83VvYKBqh8wl9KyNaRex+ehFPL9gj9xFISIimTFYeEmIn9b2fb4uHHeUvIw1po4IkIrxpfZ93KFeKWPp3JOakSt3EYiIqJ5gsKgFLSODkQ9/PGh4Br+Z+kMjmfG29ktM0PyK+jBxc9iMNXIXgYiI6gkGCy/6fcwVeHF4W9zctQkigvQwQoOnDI9hpnEkAGCCZgGmab6stbUuyLXZ69Ix6quNKCzh+0BEdLkYLLyoS2wYHr6yGVQqCQsf71t6VML7xtvxouFBmISEOzWr8KX2/Tq11sXlePufAzhyLg9Gkxmnq7k/idym/LUP6w5fwMwVh/Dwd1vx7175Z+8QEdVXDBa1JLZhgMPjH02D8ahhIgqFDlerd2K+7g00wqUKnl1/zFp1BIPeX40WLy5B32krsObgObmLVG2frjqC5H1n8cj32zx2z7Rzebjri41Yf/i8x+5JRFSXMVjI5P6+CVhm7o5RJS/goghCZ1UakvXP4hbVGtSHcRfV9d2Go7K8rtkscPvnGzB+vrwzcB7/cTs2pF3A3V9tkrUcRES1hcFCBnf2jMXkG9oDsKx1cUvJFOwxJyBMysf7us/wrfZtNEH9+U2/rjCbBUxmSyhLOZ2NzekX8cfO07KWKTO32OXxwhITigwc00FEysNgUYv+e3YgXhrRFi9f187heLqIwd+9f8A0w50oFloMUO/Gv/pncZ96KSSYZSpt/SKEwPCP/sPA91bBZBcw6iKDyYy2r/yDdq/8g6/XpiPlVN1Y7p2IyBMYLGpRbMMAPNS/GQL1GgBAt7gwAMDA1o3QKroBPjPdgGtLpmKzuTUCpWK8pv0WP+teQ3PpVCV3rds8tdq3EMDhzDzcP3szdhx3HotSbDTjQEYujl8swKlLhXWiMym70IA8u+3rraytGGYBvP73Plw3c21tF42IyGsYLGT01eieeH1kB8y4oytUpe9EmmiMo9f9gpcMDyBP+KGn6iAW6ybhcfXv0MD5Q0pJhBCYumQ/ft5yAgDww8ZjuO+bzTiUmYdXt6lx7cz1WJV6Djd9atmV9aPlh9Dm5SX4vx+2IaeobLdVAeGxQFNTxUYTOk/5FyUmtjgRkW9hsJBRw0Ad7u0Tj9AALa7tEINWUUG4p08cbu8VjzHPTsWqq/+Eufkg6CUjntX+jD90L+PKIHnHDLir/Of7igNnce2H/+FARg4AIKugBDd9ug7fbziKbccu4fPVabZdVl/6PQVrDp7D8JnrkW2QnO49PfkgigxmLEnJwAfJh2zHzQI4frFso7Xzea7HOVTGXNqVcuhsLmatOoKTlwpwodx9jpzLw+Q/9yIj23mqcGZOxa+58kCm2+Wxt+XoRcxYdhBGhhYiqoM0cheALPy0avz75ADb45hQf1w3oDcgfsOTL07CK9rv0V51DLONz+JzzXX40HgziqGTscTVI4RlvINaZQkGD87ZCgD4vx+2Y+XTV+HTVUew43gWdhzPwteje9ieZ6jiQ7P8uITMnLIP93u+2oRTdmtoPDB7C/56oh+EEFi6NwPtG4ciOtQP/+49W+FaG3/tPo2usQ0w5APLqqNv/3MAAHD4zWuhUVvy+MiP1yG32IiUU9n49f/6uryPvYk/7URogBaz1x2t8loAOJtThGd+3Y3RSfEY1DbKdvy2zzYAABoF6zGqd3y17kVEVFsYLOo6ScJCc3/8V9wJk7Xf4jr1Rjyu+RPDVFsw03gTFpt7owTaqu8jk5Wp5zDg3ZX48aHeiAn1tx3PLe26KChx3b3Td9qKSu/79C+7HB7bt4ycKhcW9pSGkL92n8G4eZbpp/f3TcCc9UcrvP/4+TtdHs8vMSHU3xIscostZd990jHkTF2yH5fyS5yeu2BH5WNlhBAOM0Ve/WMv1hw8hzUHz+HotBFO1x89n+90jIhIbuwKqSfOIxRjDeNw5pqvUOLXCM1VZzBD9ynW6cfhSc2viKzDi2udvFSIAe+uQquXljidU0llXRz24yLOVTBN09W1ALCiiu6FYxfyseHIBdvjykKF2+x6aU5eKsDnq9Pw89aTbt/m4e+2oeNry3GxtOrnqujCkSTn7iEiIrkxWNQzod1uwrbrluA9w23IEA3QSMrGeM0CrNOPw0famegmHQQgsHTClXIXtVLn80rww8ZjDsce+m5rtZ/v7nLhA95dhXmbj7v1nOoqMZrR563lmLp4P4oMNR/3sGz/WQDApkwGBiKqv9wKFlOnTkXPnj0RHByMyMhIjBw5Eqmpqd4qG5V6aURbtI4KxppnBiJAp0GxJhgfm25Cv+IPMaZkHDabW0MrmXCDegMW6CfjL92LiDj8K/Rwbo6vS176PQXfbThW9YUuWLshalvnKf8i4flF2Hc6x+F4Rk4RPl+T5tQNUxPVjRX21z3/226M/GRdlWNTiIi8za1gsXr1aowZMwYbN25EcnIyDAYDhg4divx89vV600P9m2Hpk1ciLtyy34h18ScjNFhk7oPbS17FwZGL8LNxAIqFFh1VRxG+bAJ2Bk/A05qfEI0Lld2eamD4R/+5PL7zeFaN7pduN16i2j0ckmVcRrHRhPlbTmDniSysK92T5PiFAgx6fxV+2uKdVhoiooq4NXjzn3/+cXg8Z84cREZGYtu2bbjyyrrd9K4kzRsFOh1r1aUfShp1RJ+Zi3GnehWeDf8P/jmnMFbzBx5T/4Wl5h6Yb7oaa80dINgD5jW5dutpuONMdllLh6tcIYTAG4v2o33jEIfjo77ahPV2Y0esY09e+TMFR87l47nf9uDz1WmY/UBPxIc7/7shIvK0y5oVkp1tGQ3fsGHDCq8pLi5GcXHZILScHEsTssFggMFQsx/Crljv5cl71lWRgRo83dGI9/aUvX0GgwHCbMIlhGCW6QY88dh06I4sRUbyTDTN2Y4R6s0Yod6MkyICvxgH4BfTAJxGhIy1UKav1qbX6HmScOzCMBgMEHYjVBMnLXZ6jjCbHUIFABhNRhgMBhTazbZJO5+PV/9IwZf3dqtR2bzFl/7PAr5VX9ZVmapbR0mImq1RaDabccMNNyArKwtr11a8JPHkyZMxZcoUp+Nz585FQECAi2dQdY3fUBYsPkwy4lQ+8M5uy7HpfYxQS4DRDBw5eQLDjCuRmLUeIVIBAMAsJPxn7oj5poFYZu4OA2cey+rJDkZ8kGJ5D0bEmjC0qcDkbWpcKnF/IGdisEB6ruPzWoWaMaYdx18QUc0VFBTg7rvvRnZ2NkJCQiq8rsafJmPGjEFKSkqloQIAJk2ahIkTJ9oe5+TkIDY2FkOHDq20YO4yGAxITk7GkCFDoNXW3XUdPMFaV3vDhw/H/jO5eGe3ZfGk4ddea1uUCgDyi/+Hnm/8g2tUm3GnehWS1PswQL0bA9S7cUEEY4GpP3qMHIubfs2qxZqQVWL7bkDKbtvjJh364NKG6s+SsVc+VABAVKNGGD68e43L5w2+9H8W8K36sq7KZO1xqEqNgsXYsWPx999/Y82aNWjatGml1+r1euj1eqfjWq3WK2+Ct+5b12m1Wmg0ZW+nTquFyi5YaMwSiqHDH+Z++MPcD/HGDCzufwzq3XMRXnQOD2sWA38vxgJdC8w3DcTfpiQUwE+OqvikcT+VhYpFJ9RY9GXNQkVF1GpVnf1/4an/s0IIpJ7NRfNGQdCq6+44Il/6GcW6Kkt16+fW/z4hBMaOHYuFCxdixYoVSExMrFHhyDO+urcrIoJ0mPNATwCWzbesys8sMJfr8TomohE4/DX4PXMAD5Y8jX9N3SEkNbqpDuMd7ZdICXgMP+um4GnNT7hStQuBuPxplCQfjUqZa2PkFBlse7j8sPEYrpnxHx7/cbvMpQKMJjMuulh9lcgXuNViMWbMGMydOxd//PEHgoODkZGRAQAIDQ2Fv79/Fc8mTxvQqhG2vDjY5QqM5Y/pNRVkSLUGK8zdsMLcDav+1wbGHXMReegXhBQcQy9VKnqpUgH8AZOQkCISsdncBlvMrbHF3BqX4LmuLPIulQdW6czMKcLPW0/g9p6xiAyuG61ZnSb/CwDYO2UYvvgvDQCQvO+s2/c5n1eM95am4s5ecegSG3bZ5Rr56TqknMrBiqcGoFmjINvxEhNQbDRD4b/Yko9zq8Vi1qxZyM7OxlVXXYWYmBjbn59++slb5aMqVHdZZ71Gjb+f6Ofy3BsjO2DikFZISGiOFje9jJBndgFPbMePUU/jN1N/nDA3gloS6KxKw8OaxfhC9wF2+D2Gpbpn8brmG1yvWs+1Muo4o9n9MdqLdp/BwbO5tsf/+3Yr3vv3IB7+bttll2fGsoP4YNnhCs/nFBkw+c+92HasekvV7z6ZjRMXa96q9uLCPZi/5QRGfrKuxvewl3LK0hf9164ztmPFBhOe2axB0turPPIaRHWVWy0WNZxAQrWkqrenQ5NQqCTLtuL27ulTbodMSQLCm2Pkg5PQ/tWlAIAYXEBP1QH0Vh1AH/UBNJdOobXqJFqrTuJeLAMAZIgG2GlugZ3m5tgpWmC3uRnHadQR7rZX/HfoHMbMtXQpJD95JdYcOm/bzG3XiazLKktesREzllm2uX+zh/P5ZfvO2pZ3n7P+qMsN2Mp7a/F+h8cHMnLQJrr6LWqHM/OqfW1NHb9kCT65RUYIIbjXCykW5xgqSItIS5NriF/Fb6skSVUnkFKB+rL7nEE4/jRfgTV+V2HjpEFAySU8+ubH6KU6gF6q/WgrHUe0dAnXqLfgGvUWAIBJSDgomlrChrAEjkOiKcxcoKvWba3mb/5W1t+4Adi2jvcUk6ns35+p3D/FYqOpwj1j/kk5g5+3nsR7t3VGw0Cdwy86Ry84rv57zYz/qhVIAMteL0fOeWf1YIdxT3bHzQJQM1eQQjFYKIifVo19rw1zmGZ6uVY+fRV2n8zClS0b4aetJ3Bjl8bw06oBbQSWmntiqbknQv21CFGXIDo/FV1Uh0v/HEET6QLaSifQVnUCd2ElACBP+GGPuRn2iXiki2ikiRikm2OQgQZcEdSLsgsNKDaaoNeoZS1HyqlsXDezbIr69D1q3HmjQInRjJs+XYe9pyuezvbYD5YWlHeXHsDUmzs5nHPnX7zZLHD3VxvROMwf02/vghnLDrpVB4PJbJt1IoTA6ewiNA71c9kCsWj3GUwY3Mq5DEJA7XY7ElH9wGChMAG6yt/SsQNb4MPlh3BztybVul9iRCASIyxLQT82oLnDuVB/LbILDeid2BBPXN0S93ytw5bCNoDJcr4RLqFracjoKh1GR1UagqQiJKn3IQn7HO5VKHQ4KqKQLmKQLqItX82WrxcRDPcb86k8o0lAX0v/409lFeKOzzdgdFICHr6yme24fagAgKwSCe/8ewiL92TgdHZRte59Ps8y28K+4a26YfrExQK89HsKNqZdBABMv70L/tp92uGaB2Zvxjf393QICu//m4pTWYW4p0887vx8I27q2gRv39oJH684jPeTD0KtkvDzo0noHt/A4V6H7LpY7O9XfpZWbRBCIKfQiNAAjhwl72Kw8DHjB7XEkHZRaBMdfNn3+mtsPyzYcRL3901AWIAOO14egru+3IhN6ZYf2ufQAP+ae+Jfs2U6rApmtJROorPqCFpKp5AgZaCZdAZxUib8pRJL6wZOOL1OtgjAARGHveYE7DEnYo9IRJpozC4VN83bfBxfr03Htw/2Qqsoy/tfUV9/VR98aw+dR7+WjkvCG01m7D2dg/aNQ/De0lScvFSINxfvx4P9Eiv94P9q7dEqy26/a6u1aPYldHX/ghKjU9C+9bP1OJtTtsWA0WR26hlcmXoOecVGBPtZPoB/2nIcM1dYBpou2H7KcmzrCbSODsb7yZbWDpNZ4JZZ6112vxy/UIDYhv6wL2Jt5YoigwnvLU3F1W0j8deuM5i3+Ti+fbAXBrRqVDsFAJBVUILP16Th5q5N0DLq8n/uUN3HYOFjVCoJHZqEeuReceEBDs28KpWEuQ/3wfGLBRj43iqn681QIVXEIdUU53BcDROaSueQKGUgUTqDRCnDEjpUZ9AYFxAqFaC3ZBk4alUg9Ngn4rHHnIiU0rBxRDSGCfI29ddlbyyyDHB89tfd+H3MFZi3+TimLt6P2Q/0wgfJBxEZrMf0O7oAAN5dmlrpve75epPTh+gbi/ZjzvqjuKdPnMNmbJ+uPIwnBrWscbm3Hr2IWz/bYHu8bP9ZLNh+Ep2a2v87dg4Wn606golDW9seZxcYHEIFALR4cQmahDlPlZckCUII7D2dg+d+2+OyXK/9vc/l8d93nHJ4fOW7lm7Ar+7tajtWWy0WX69Nx1elf6xGf7O52uNPPOHlP/bir12nMWvVkVp9XZIPgwV5lFol2bpOqssENY6JaBwT0ViFLg7n9ChBgpSB9tJRdFAdRQdVOtpLRxEoFaOHdBA9VGX944VCh/0iDinmRGQhEAahgQGWPyWlX63HSqCBEWoYoEGR0OEUwnFGhPtEMCkssfRVTVpg+cC8ZdZ627m3b+2EheU+GCuS8PwirH/+apjMArENAzBn/VEAwA8bHbdqfz/5IMKD9Fi+3/31JQA4hAqriT/vKnfE+YP6VFYRDmTkYNm+s5iefNBpNlTZdc7TVDu8uhTTb+/s4nUqt2j3GUz4aafLcw99v8P2fQ1m/9bI0fPeGZTqjsudRUT1D4MF1WnF0FlaOUQcFpivBGDpUkmUzqCjlI6OqnRb2AiSitBNOoxuqorXR6iMQahxSkTguIi0/TkmonCi9Gs+lLEIXOrZXBTY7X5q74s1aVW2VtjrO20FAODDO7tUet0LC13/1u8p1nEX9pbtP4vftp+s8T0/W33E7edYp+hWpbam7nOBgDI7T2RBr1GhbQwX9vM2Bguqd8xQ4YhogiOiCX43Wxb9kmBGopSBDlI62qqOwx/F0MEILYzQSqVfYSr9aoRWMtnOB6AITaQL0EsGJEhnkQDXv1lfEME4LqJwXERaBpiWDi49KqKRA/daaeT22l+um/F/3VazD+Lx83deRmm8I7vw8raxvuAirHiKqZaaLLj0kEVWQYlt8bP1z1+NI+fy0L9l7Y0z8TUMFuQVYwe2wMcra9ZyUBMCKqSJxkgTjfGn+Qq3ny/BjChcQpyUiXjVWcRKmYiXMhEnZSJOOotwKdf2pyuc63VehJSGDUvQSCsNHEdFFIrgvAmf3OZvcR4kCwDpdaDpvK644MW9PkqMvruF/d7T2fhh4zE8ObgVIkNqtoBedoEB6Rfy0blpaLUWGjuXWza2xtrK9sP/ejsNQCbPYLAgr3hqaCsMax+N6z9eW/XFXtIkzN9l/7krAipkIBwZIhyteg7D9HLjBIJQgDgpE7FSpqVVo3RwaYKUgSgpCxFSDiKkHPRUOa+JcEqEwxjWHKsuhJaGnxgcMTfGGTTk2h0+6uu16Zg0vK3XX0d4qDPko+WHkHIqG7Pu6Q61SsL3G49h1srD+P6h3mhutxdKdYz4yPIz4eSlQsy+vyc0FexEm1VQgsd+2IabuzVFt7gwNG0QAL1GBUmSMGj6KpzPK8Hs+3tiYJtIAJbupcl/7kViRCDuv8Jxg0xX4WNz+oV6HywKS0y4WFDicgCynBgsyCskSULHpo6zTx64IgGz1x297HvbL0u++cVBiAz2Q8Lzi5yu+/ze7k7rJlTHGyM7Og1AzEMA9okE7BMJTtcHohAJUoZtVkuCKgPNSr8Pk/LRRLoAZF/A6HL/2wqFDukixhI0SsNGmojBcRFZ2rXCtTuUalXquVoJFhXlii/WHMHPW09i/iN9EBFUdYva9NJptf/7dguu69QYL/+eAgB4+fcUzH24T7WLM8Zu59n/Dp1HixeX4N1bO0GtkrAy9RzCA3WYfEN7AMCHyw9hY9pF25ojABAfHoC/n+hnG1Pz0YpDSDufj/uS4rHjeBa+3XAMAFwEC+eyKGFJ9aveW4mzOcVYNnGAbeXluoDBgryqTXQwDmRYNrKSqvlBObhtFJbZzSB4a2Q7vPC7ZUzAHT1i8fatnZCakYtAvdq2y6YbK5UDAP5+oh92nczCiwtTqv+kCuTDH3tFIvaK0h9mprJzYchFM+kMFtzeCHmn9mPXzq2IKjmOOOks/KUStJOOoR2OOd2zRKhxAaG4IEJwQYTgPEJwQZQ+RgjOC8fHxdBddj2o9hjN3u8Kmbn8EBZUMMPnrcUHbNdMubFDpfex70ZYlXoOq1LP2R4fzsxD8r6zGNw2ssIPavvDi/accTr/zK+7HR5f3zkG3eIaIKfQeYDxsQsF+GVr2TigHcezsON4Fk5eKkDLyIrXyHC1u+/hzDz8uOkY7uoZB5UHVyuuTdbp0ysPZDJYkO/o1DTUFiz6NGuIb9alV/EMoEmYY7+rfWC4snRhn9blFviaMKgVPrBbmvm92zojtkFAha8REaRHeKD3P4yzEIztIhjoOgJBXYHo3nkY9P5qqGFCrJSJZtIZNJdOo5l0Bs1Ulu8jpBzoJBNicBEx0sWqXwSWpdIviuDSMBKMiyIEF0sDyEURjIsIsYWUXASgEDoY+d9fNpUN3hRC4HBmHhIiAmEWosbLsFsX76rMtxuO4a7ecbaA8PYtneCnVeNSfglOZRXiXG4xHpizpcLnZ+YW4+HvtuKLe7tjaPtop/O5RUYcu1DgVrlvmbUB/VtGYP0R1zsmFxpMTseqagl1FRsW7TmDRXvO4NOVR7Du+avdKmNdY9/llVO6hkyQTiNbYOJPFvKqsQNb4o+dp3F37zgMaReFr+7rgTYxwej39kqX1784vC3iwwNsTZrlVdR6+cTVLTCwTSPc8LFl5Hf3+AYIDdBi+VMDoNeobK83pF0UrmkfjehQPwwNjsZ9SfE4k12E7AIDNh+t+kN8cNtILNufCQD44t7u2HrsEr5dfxTF1RyMZy2+CWocFTE4KmKwAt0crtGjBA2Ri3ApGxFSDsKRg3ApG+Gl4zjsH4cjB3rJiCCpCEFSEeJwzvlFK1Ai1CiCHoXQoUDoUQQ9CqBHodChEH52x3UohhbF0KJIWL8v/Sp0KHJ4rIUKZmhghloyQQsT1DBBAxPUMJc9lizHBCScFI2Qbo7GaYT7zJiTyrax/2XrSTz7m+W3eK1awvrnB6FRsHsDgN9deqDqi0rd/tkG5BRZWgfaxoRgdFICur6e7NbrbU6/iKHto5GRXYTR32zGjV2i0QTA+8mH3LqP1X+Hzld4rrrToQtLTHj6l104eDYXd/SMrfC66o7DktOag+fw9dp0TL25IxqH+SP9fD4qygydp/wLIcq6ieXAYEFeFRcegL1ThtkGaA1uF+V0Tbe4MNzYpQnu7h0HrVoFs1lAq5ZgKN36sjo9HCqV5HJ+evmBZcPaR+OW7k1tz3mttBn4tb/2VStYtIoKtgWLwW2jMLR9NF6w6yt3NdbDoZzV6Ncthg5nShfsqrryAsEoRLiUXRpGchAu5aAhckpnsTgf10mW3/h0kgk6FCAEBXViOEex0OKYiLTtF3O0dJZNuohGJsJQJwrpIUaTqHBTuK/tVsk0mAR6vrkMCx7vi+YRQQ77fJjNAiqVZNsUbeJPO3GpoATf3N8Tn6ys/hoc1lABWJrUpy2pfiixkiRLefpMXQ4AeGdpLj5MsqyZIocigwm93lyG3GJL3ayrztZX932zGQDw3G+78eV9PVyubGxVF6YYM1iQ17ka9R0fHmBrIp0wuJWtiwOwfOAfenO47UO6h93GTmH+l7eBUnAFW8qPvboFNqRdwC3V3JzNWs7quMauiTi2YQCaNQrEuZxi2w+9ytj/PbkmIRcByBUBOIqYagURHYzwRzH8UYwAqRj+KIGf7fvSP1IJAkq/10sl0MMAP1i+6qWy7/1QAr1kcDhvggpGqGGEGiaoYIAaptLHRqF2OK+ByTalVy8Z0Eo6hVZwHheQL/Q4KqJxUjRCHvxQJCwtLYWlLSzWlpfC0uNFpcfz4YdsBCJHBCIX/nWmRSQjpwgdX/0Xm18chLAAxy45Vx/GN3+6HpIEHHzjWmjVKvy85QRe+3sf8kr/DX10V1fbeIonK1j5szqs+/y4y2AS6DTl3xq/rqcdPJtbrf9ftelwZh7WHT6Pu3rFQaep2b/DzJxiZBU4r88ihGWAbb5dnas7ps0bGCxIFj/8rzc+XXUYQ9pFOYQKe9tfHoILecVIaOiHu5ub4B/dDEnNw2v0eq+P7IBdJ7IwuK1ziwkANAzUYcn4/i7PdWoait0nswG4v5LhuKtbYLzdfipqlYTkJwdAAtDshcUAgGeGtXZq3n16SEtoNWo8OqB5la0ggKUJ+6M7u2DIB2uquFJCCbQogRbZCHKskIy/6ahhQmPpvG2fmMTSPwlSBppK5xAoFaO9dAztXQx0rS6TsISwLBGEbAQiWwQip/RrNgKRJQKRhSBcEsG4IEJwCcG4KIKRgwCvBJISkxlL92bgjp5le+d8UsnaL0IALV9cgm/u72HrKrEaN69sufDfd54u/1Svsy7nbq/ACBw8m+d8cS2wdonWBesOn8e+0zl4c7Gl1aTIYMKjdjtFp5/PR1SIHkazQF6REedyi9E5NszlvVLP5uLrtWlOx41mgY+WO3Y7yTnphcGCZBHbMABTb+5U6TUNA3VoGKiDwWBA70iB4de0rnSKmNruXMNyvwXe2yce9/aJr1FZG9oN8qyqmfGr+3rgoe+22h7HNgxw2nmz/OMAndppKu6jVyZCq61+68z3/+uFhgE6tIgMgtksEBPmh3WHXQ9+q4tMUOOEiMIJEYU16OxwTgtj6SZ1Z9BYugA/lNhaVexbWPxRbDlX2grjj2IESYUIRT78JAPUkkAY8hEmubcImElItpBh+yqCcRHBKBB+tn1ojFBbvhdl+9DY70lTIjS21hpz6dfC01ocPZSN6NAg+Ol1mLN0ExpCBSNUDrv3SgAkCAACE+asQmjpY6k0DUoAVBDQlbYo6WGAvrQFSScZbd/r7c5rYEIB9MgX/siDX9lXu+8LoYf7XVACapjxwhYVBOpWq4GnHLuQD71GjejQqscwjPpqk8PjHcezcDG/BPfP3oyE8ED8ucs5CC6beCValM5yMZocx299+Z/zAPjzecVOx+TsOGSwIMVQqST8/UQ/FBvNDn3RNdE5Ngy7TmQh1F/r8B+0qgWHBreLwusjO9jm+Ve3EeC5a9ogOsQPU6vZv/3X2H549c8UtI0JweQb2kNb2t20dIJlPxW1SqpWS0d9YICmdNxFTI3voUcJQpCPUCkfoaVfw5BneVx6LETKRxjyES7loAFy0UDKRYhUCLUkEAHLwFmP21H6p9QWecbaVcgkJEvQgD+KhBZqmKGWzLbBuJaoVDpY1zpIV7L8qzcLCdkIxCURhCwEIUsE4RKCkCWCXR7Lgx+Khba0RU1j+1qrH5FmE1CSDxgKAEkNaHSAuvSPyjIeJrvQgAHvrgKAGu/W+vGKw9h9MtvWElre7pPZtmBx79ebK7mTgBYm/LJuH8JLg6OuNDhKwnn2TG1hsCBF8dSW8J/d0w2frTqC+/omYO/pHKxMPYeWkUHoldAQn692boq0d2+feFuwqCxZhPprkV1owJWtGsFPa+n2GN4hEmtXraiyfB2bhmLB485Ll9u3htzVKxbzNrteutvXFEOHc9DhnCgdr1PNxKeFEWGwLOXeQMpFQ5R9bSjlwB8l0ErG0sm7dnvRwAStZIQGRujsjqtLZ8tYP4jVMJf7gC77YK5RPYXWNoPHOkun2G5Wj/15E1QIRDECUYhAqQhBpV8DYfmjkgTUkkAIChGCQrc/31WSQAPkoYF0ed0hljKXBY0S4Rg8DNCgWGiddjIutu1kbPmb18NgqZtUhEAUIkAqttQZRTC+PR5qYz4kQyXjmSQVoNYjSKXFVj0soefDF6BRaXFVfhHUZ963BBGVBlBpkFUMaLRafKHNto0pMkCNhqcDUHwSaKspsoU0jcPMKSOil2mxZ3EJmjXU4bmMLGh1pnItTiXQwQg9DFBV8O8lq+hGILjiKffexGBB5EJMqL9t4aBmEYGIaxiAFpFBCNSp8dV9PZzW0ahIZS0c65+/GpcKStDUbr2N6BA/BHjof+WUGzpgZJcmuOOLjZ65oQ8yQINzaGAJJLU2BkXYwoblkWT7Kip57Mnf7CWY4Y8SBKIQQaUfxH4osQ3CtXbplH1VwyhUMEFVeo0KOpgQJuWWhotchEl5CCsNGtavDaRc2/cBKIIOBtusJStr1w1Q6OlqlnGacSrB6Q0XZsBYCDUKEWEtw6WLkACEAkCGY4gPK/06tPzEH+trVfb/3JpvMoEubgzvsQ+OOrN83VAMFkRVkCQJXewGU7maMlteq6ggHDybh4GtIyu8JlCvQaC+6v+Cy58agNWp55CakYuftla/BUKnUaF3s5oNdvUFVc+4kYtUutpHzRbG8gQBFQrghwL44dxlBKrzItR6w2qTYLb9Nq6DsTRs2H0PI/Slj7Wlx7QwQlfaclR2zFR6zHK+GFrkC0u3jmUciZ/dV3/kQ4//XroB0AVixeFsPPjtVoRoBeY/2A3tovyxev8p6GBAuJ+EsT9sghZGLHq8F4zFBdi8YR3adOiIZ3/ZCQ1MiA/T4mxWfmmrVVlLhHUtFxXMzjOmrEFNWI4Z4DiDqkjoUOKiNaoEGhSXtt7YDzLeGey8YFltYbAg8oLF4/qjwGBCiN/ljfUALGtxNG8UhBUHzuKnrSfgp60bUybruzt7xuHtf9xfs4G8S0BV2n2jsz9Y/iLvCLRsSvbgtysASMgxSHjk51QseqI/Rv9i2T9o4eN9cVBYBlyKpj0hjEac25uNu39SA+huuU/NZu16lJzTTfkTisgLNGqVR0KFvYGtI/HTI32w9rn6vfxwXdCnWUM8cEWC3MWgOuaWWeux4/glh2OFJSaHxfNSTpcN4n1i3g78vfsMKllIVT4yTgthsCCqJyRJQu9m4dXajbI+eaR/gsvjXSqYy+8Jg9tGwU8rX1cD1U3bjl3CTZ+udzhWbDTjYbsp5LaB2QD+3n0GT/6yBwuO1r2PUjnXsah7fxtEpAivXNcOIX4a9K1kUbOJHY14anBLl+dm398T8eGWga2v39je6XxoDVZh3fLiYHx2T3fc3zfB7ed2jQuzfR9TjfULSBnyqrGC538Zde+jVM51LOre3wYReVSArvZ+M3/n1k5oUzpj5tYeTbF78jAkVTKAND7IeWn0N0Z2wJQb2qNBoA6rnxmIo9NG4N6kBKcP812vDnW6n/0qrrMf6ImUKcMwsHXZsUbBelzTIdrlMvM/PdKnwnK2iQ7Gq9eXhZs5D/RCn2YNbY97JTTEraV70BDVBZUtJuhtHLxJpHBzHuiF2z/f4PLcfUnxto3Y7BfUigjSu1zNL0ivqfQ3uITwQCwa1x8lRjP83Qg0M+/qihcW7MEno7pVuMT7E1e3xAsL9wCwrExY3p09YzHtlk4O9QjSa6rdslHRD+Jnr2mNB69IdKh36+hgzH8kCYBl1cMGATqoVRKOXyhwuZmd/bLwRErHFgsihevUtGzRsJu6NsGfY8sW1nI1XiMxIhCf3VO2lbtOo7L9Nv7x3V1tx0d0isE39/fAnsllLQdatQS1SnIIFfbj2lpGBuGOHrF49MpmeHpIWRfI9Z0bY9erQysMFQBwd+84rHr6KqS9Ndy2KuG8hy2tDOOuboHJNzh2l1hjgqtdb63sN7jT220MNfXmjgAsYzEev6oF/LRqRATp8dfYflj+1ACHe0QE6W0Lk9mvW9IsItD2/ef3dsfopDjclijfaojkW7ikNxF5jZ9Wjf+eHQjAsneJPfvNjkZ0jMGiPWcwYXBLtIwsWwBs96tD4adVY+rNHaFVq9AroSE2H72I+/rE29bJGD+oJU5cLHA54LKPXVfIv09eaWsZMBgMWLy4bDvr6uwWm2D3YQ0ASc3DK1xW2fo6D1yRCKNZYICL0GLfSGEfwO7qFYfbujd16jLp2LTylV3DA8uCmn19YkL98dLwNli8OA2/OG/1QORx3ISMiLyqfKBY+fRVOHg21+HD9qO7uuL5a9vYrt38wiDotWrb7AnrfiQ/Ptwb53KL0TjM3/bcJ4e0QkV6JTbET4/0QUJEYK32+1pfSadRYczAFi6vefyqFnhgzhaM6BQDSZIcQoqrcRhVmXxDe+QVG3FfUjwkScLD323FfUk12/zOU1QS6uZ0SA9r3zgEe097YT+XeqqqDRO9icGCyAclRgQisdxv/2qV5BBAIkNcz3zQqlUOoaI65FgBtH3jirtArAa2icTmFwchItAzU3ijQ/3ww0O9bY93vDwEYZe5IV5N2I+R+e+5q/HaX3uxdO/ZGt2re3wDbDt2qeoLZRIZrEdmbjHeusnSfXXjJ3Vny3RfxTEWRKQou14dio2TBiG8mut9RAb7VasbpiYaBOpkGZ2/9aXBWP3MVdgw6Wo0CfPHlBs6VOt5d/SIdXg8Oikevz6W5LSY2BNXu24BqsgbI6t+fZ2meh9H/VtGODxe+9zV2D15KDrHhqFzbBiOvDXc5fNeGtEWr7mYtqxU1dkuwFsYLIhIUUL9tYiuw+tMjOxc8+3f3REfHoiYUOeWpeEdK95DopXd5nptooMx5cYOkCQJr17f3mEdj6eGtnYY4Gvv0QHNnI7d0ycecx/u7eLqMu/e2gn7X7sG8x7ug5Qpw2zHo0L0+PuJfugWF4YnB7fC9/9zvI+AcFjlVq2SnAbsRoXo8VD/ZrgvKQEtIoMqLQddPgYLIqJa9M4tHbDrlaFY8HjfSq9b+9xAzH24NxaP63/Zr2nfaDL15k449Oa1WP3MVU7X3dilse17c7lO+um3d0GvhIaY80BPAK778JOahWPStW1tj+MbBuDDO7sAAPo2j8C1HSyhJkivwbhBLfHHmCuQ9tZwrH/+atzYpQn8dWokNQ9HkN1v24F6DTo0CcWCx6/AeBeLqWlVzh9jCx/vi3du6eRcQACTr/edVgu5cIwFEVEtkiQJoQFadItrUOE14YE6NG0QgKYNLGNe3rm1E579dbfDNTPu6AKTWcBoNuPohQLMWnUEAKpeVVRYxsnEh5eNsXnrpo64pXsT6DVqjOodhx83HcdTQ1s7PC0xIhA/P5ZkezygtfMsG2t3xuTr2+KP9Xvx4xN9EeBX1iU19eaOaBcTgpFdmziM56lszE6zCOcWhi/v64GHv9uKd27t5LIby0+rxu09Y/Hsb7udzvVrGYHP7ukOQOCxH7ZX+LpUcwwWREQyua17U/yy7aTt8YqnBiDtXD4Gt4tyuO72HrG4tkM03v/3IOasPwoACPHX4Oo2ZddZg4XexVgF+31RtBrnD+JmjQKh11iueWNkB0wc0qrKMSoBOg0Ov3ktzAJo9dISAGUtI6N6xaLB+T22mURWYQE6PDHI9RLu5S14vC/mbTqOZ69p43RuSLsopL01vMZjY67pEA1zPZsq42rRuo/v7oqF20/hnj7xeGDOFtvx7//Xq7aL54DBgohIJu/c2gnPXdsGD3+3FeGBejRrFIRmjVyPAQj202LyDe3x+85TyCowoGus6xYPV4NFQ/21lt/uJQkBurIf+wse74vDmXkOa41IklTtga/lp+RqPDgItltcg0pbddwJFa2jnWcIeWvArjsmDG6JGcsOOR2/t088vt94zPb4nVs64bYeTZE4abHtWESQHiM6xuC6TpbuqyZh/jiVVYjZ9/dE/5YVLzRXGxgsiIhkIkkSIoL0WPB/fas9e2TjpEEoMpgQFqCr4J6un3d7uRkfQNUf3tX1ynXt8NV/aXj5unaXfS9PWjSuH37cdBwTKtjorjbdlxSP7zZYwsLtPZrimg7RMJpct5pMuaE9dp/Mwq7SZeD9dWqnfx9bXxrs8HjJhP44dr4AHZpUPc3a2xgsiIhk5s6UVD+7RcvsqVUSTGbhNB2zNjzYLxEP9kus9detSvvGobb1LeRy+M1rkXY+Hy0jg/DcNW2gUUu2bqd/Us7Yrlv9zFWYuvgAwgK0UKkkh6XwB7e1dHl9fm93TJi/E9Nv7+z0OiF+2ipXhq0tDBZERAqwcdIgHLu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       "   </g>\n",
       "  </g>\n",
       " </g>\n",
       " <defs>\n",
       "  <clipPath id=\"p3773c11245\">\n",
       "   <rect x=\"20.5625\" y=\"7.2\" width=\"357.12\" height=\"266.112\"/>\n",
       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([i[\"step\"] for i in record[\"train\"]], [i[\"loss\"] for i in record[\"train\"]], label=\"train\")\n",
    "plt.plot([i[\"step\"] for i in record[\"val\"]], [i[\"loss\"] for i in record[\"val\"]], label=\"val\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4，感兴趣的同学自行了解并实现\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dncrLyyVJTqfTljdtAE0JZ3L+1xQe+/w1A5EDABiLpysBAMYicgAAYxE5AICxiJwfuFwu5eXlyeVy2T2Kz7CmwMCaAoOJa5Ka5rp444kf1NTUKDY2VtXV1X7/af7GwpoCA2sKDCauSWqa6+JMDgBgLCIHADBWiN0DNBa3262dO3eqWbNmnl8a6i81NTVe/zUBawoMrCkwmLgmqfHWZVmW9u7dq/bt2yso6PTnamfNa3Lffvstvz0EAAxSUVGhDh06nHafs+ZMrlmzZpKkAc2HK8TR+L+yxl8OXNz4v+Hf//x7pm2HukjzXhn4qVOw3SP4Rch+uyfwvZalB+wewafq6lxau26G53H9dM6ayB17ijLEEWZU5EJCwu0ewff8/HSyLULNi1yw08zIBR+xewLfCwkx8wm7hrz0ZN5XHgAA/x+RAwAYi8gBAIxF5AAAxiJyAABjETkAgLGIHADAWEQOAGAsIgcAMBaRAwAYi8gBAIxF5AAAxiJyAABjETkAgLGIHADAWEQOAGAsIgcAMBaRAwAYi8gBAIwVEJFLT0/XxIkT7R4DABBgQuweoCGWLVum0NBQSVJSUpImTpxI9AAAPysgIhcXF2f3CACAABRQT1emp6frm2++UXZ2thwOhxwOh92jAQCasICI3DHLli1Thw4dNHXqVFVWVqqysvKU+7pcLtXU1HhtAICzS0BFLi4uTsHBwWrWrJnatm2rtm3bnnLf/Px8xcbGeraEhIRGnBQA0BQEVOTORE5Ojqqrqz1bRUWF3SMBABpZQLzx5JdwOp1yOp12jwEAsFHAncmFhYXpyJEjdo8BAAgAARe5pKQkffTRR/ruu+/0ww8/2D0OAKAJC7jITZ06VeXl5erUqZNatWpl9zgAgCYsIF6T++CDDzx/7tOnj0pKSuwbBgAQMALuTA4AgIYicgAAYxE5AICxiBwAwFhEDgBgLCIHADAWkQMAGIvIAQCMReQAAMYicgAAYxE5AICxiBwAwFhEDgBgLCIHADAWkQMAGIvIAQCMReQAAMYicgAAYxE5AICxQuweoLEduPhchYSE2z2Gz7hDzfs+5XC0eWvae3ON3SP4XEKeZfcIfhH0U63dI/hecLDdE/hUkNvV8H39OAcAALYicgAAYxE5AICxiBwAwFhEDgBgLCIHADAWkQMAGIvIAQCMReQAAMYicgAAYxE5AICxiBwAwFhEDgBgLCIHADAWkQMAGIvIAQCMReQAAMYicgAAYxE5AICxiBwAwFhEDgBgLCIHADAWkQMAGIvIAQCMFXCRe/XVV9WlSxdFREQoPj5emZmZ2rdvn91jAQCaoBC7BzgTlZWVGj58uGbMmKHrr79ee/fu1ccffyzLsurt63K55HK5PJdramoac1QAQBMQcJGrq6vTDTfcoMTERElSly5dTrpvfn6+pkyZ0pjjAQCamIB6urJbt27KyMhQly5dlJWVpWeffVY//vjjSffNyclRdXW1Z6uoqGjkaQEAdguoyAUHB+vdd9/VW2+9pbS0NM2ZM0cXXHCB/vWvf9Xb1+l0KiYmxmsDAJxdAipykuRwONSvXz9NmTJFmzZtUlhYmJYvX273WACAJiigXpMrKipSYWGhrrrqKrVu3VpFRUXavXu3UlNT7R4NANAEBVTkYmJi9NFHH+mpp55STU2NEhMTNWvWLA0ePNju0QAATVBARS41NVVvv/223WMAAAJEwL0mBwBAQxE5AICxiBwAwFhEDgBgLCIHADAWkQMAGIvIAQCMReQAAMYicgAAYxE5AICxiBwAwFhEDgBgLCIHADAWkQMAGIvIAQCMReQAAMYicgAAYxE5AICxiBwAwFghdg/Q2MIKixXiCLV7DJ8JTulk9wg+V/202+4RfM61qY3dI/jcT2mW3SP4RdxH1XaP4HuHDts9gW+5DzV4V87kAADGInIAAGMROQCAsYgcAMBYRA4AYCwiBwAwFpEDABiLyAEAjEXkAADGInIAAGMROQCAsYgcAMBYRA4AYCwiBwAwFpEDABiLyAEAjEXkAADGInIAAGMROQCAsYgcAMBYRA4AYCwiBwAwFpEDABiryUfu8OHDdo8AAAhQPo3cggUL1L59e7ndbq/rr732Wv3+97+XJK1YsUI9evRQeHi4zjvvPE2ZMkV1dXWefR0Oh+bNm6ff/e53ioqK0vTp05WcnKyZM2d6HbO4uFgOh0Pbtm3z5RIAAAbxaeSysrJUVVWl999/33Pdnj179Pbbb2vEiBH6+OOPddttt2nChAkqKyvT/Pnz9eKLL+rRRx/1Ok5eXp6uv/56bd68WWPGjNHvf/97vfDCC177vPDCC7r88suVnJx80llcLpdqamq8NgDA2cWnkWvRooUGDx6sxYsXe6579dVX1bJlSw0cOFBTpkzRH//4R40aNUrnnXeerrzySk2bNk3z58/3Os4tt9yi22+/Xeedd546duyo0aNHa+vWrVq/fr2ko09hLl682HN2eDL5+fmKjY31bAkJCb5cKgAgAPj8NbkRI0botddek8vlkiS99NJLGjZsmIKCglRSUqKpU6cqOjras40dO1aVlZXav3+/5xg9e/b0Omb79u3129/+Vv/zP/8jSfr73/8ul8ulrKysU86Rk5Oj6upqz1ZRUeHrpQIAmrgQXx9w6NChsixLK1euVK9evfTxxx9r9uzZkqTa2lpNmTJFN9xwQ737hYeHe/4cFRVV7/Y77rhDI0eO1OzZs/XCCy/o5ptvVmRk5CnncDqdcjqdPlgRACBQ+Txy4eHhuuGGG/TSSy9p27ZtuuCCC9SjRw9JUo8ePbR169ZTvo52Otdcc42ioqI0b948vf322/roo498PToAwDA+j5x09CnLIUOGqLS0VLfeeqvn+kceeURDhgxRx44dddNNN3mewvzyyy81ffr00x4zODhYo0ePVk5Ojs4//3z17dvXH6MDAAzil5+Tu+KKKxQXF6etW7fqlltu8Vw/aNAgvfnmm1q9erV69eqlPn36aPbs2UpMTGzQcceMGaNDhw7p9ttv98fYAADD+OVMLigoSDt37jzpbYMGDdKgQYNOeV/Lsk5523fffafQ0FDddtttv3pGAID5/BI5X3O5XNq9e7fy8vKUlZWlNm3a2D0SACAANPlf6yVJS5YsUWJion766SfNmDHD7nEAAAEiICI3evRoHTlyRBs3btQ555xj9zgAgAAREJEDAOCXIHIAAGMROQCAsYgcAMBYRA4AYCwiBwAwFpEDABiLyAEAjEXkAADGInIAAGMROQCAsYgcAMBYRA4AYCwiBwAwFpEDABiLyAEAjBVi9wCNzrIkWXZP4TPWN9/aPYLPOWdeaPcIPvfVonl2j+BzvR66y+4R/OPIEbsn8L3gYLsn8DFHg/fkTA4AYCwiBwAwFpEDABiLyAEAjEXkAADGInIAAGMROQCAsYgcAMBYRA4AYCwiBwAwFpEDABiLyAEAjEXkAADGInIAAGMROQCAsYgcAMBYRA4AYCwiBwAwFpEDABiLyAEAjEXkAADGapKRGz16tK677rrT7pOenq6JEyc2yjwAgMDUJCMHAIAvEDkAgLH8Fjm3260ZM2YoOTlZTqdTHTt21KOPPipJ2rx5s6644gpFREQoPj5e48aNU21t7SmPtW/fPt12222Kjo5Wu3btNGvWLH+NDQAwiN8il5OTo8cff1wPP/ywysrKtHjxYrVp00b79u3ToEGD1KJFC23YsEFLly5VQUGB7rnnnlMe64EHHtCHH36oFStWaPXq1frggw/0+eefn/bju1wu1dTUeG0AgLNLiD8OunfvXj399NOaO3euRo0aJUnq1KmTLrvsMj377LM6ePCgFi1apKioKEnS3LlzNXToUD3xxBNq06aN17Fqa2v1/PPP669//asyMjIkSQsXLlSHDh1OO0N+fr6mTJnih9UBAAKFX87ktmzZIpfL5YnSibd169bNEzhJ6tevn9xut7Zu3Vpv/+3bt+vQoUPq3bu357q4uDhdcMEFp50hJydH1dXVnq2iouJXrAgAEIj8ciYXERHhj8OeEafTKafTafcYAAAb+eVM7vzzz1dERIQKCwvr3ZaamqqSkhLt27fPc90nn3yioKCgk56dderUSaGhoSoqKvJc9+OPP+qrr77yx+gAAIP45UwuPDxckyZN0oMPPqiwsDD169dPu3fvVmlpqUaMGKHc3FyNGjVKeXl52r17t8aPH6+RI0fWez1OkqKjozVmzBg98MADio+PV+vWrfXQQw8pKIiffgAAnJ5fIidJDz/8sEJCQvTII49o586dateune68805FRkbqnXfe0YQJE9SrVy9FRkbqxhtv1JNPPnnKY/3pT39SbW2thg4dqmbNmum+++5TdXW1v0YHABjCYVmWZfcQjaGmpkaxsbFK17UKcYTaPY7PBIWH2z2Cz7kuu9DuEXzuvUXP2z2Cz/V66C67R/CL1m99bfcIvhccbPcEPlXndqlg53xVV1crJibmtPvynB8AwFhEDgBgLCIHADAWkQMAGIvIAQCMReQAAMYicgAAYxE5AICxiBwAwFhEDgBgLCIHADAWkQMAGIvIAQCMReQAAMYicgAAYxE5AICxiBwAwFhEDgBgLCIHADBWiN0D4NdxRETYPYLPRZR+Z/cIPnfR9LvtHsHnnnjoWbtH8Isp+8bYPYLPxX74td0j+JTlPtLgfTmTAwAYi8gBAIxF5AAAxiJyAABjETkAgLGIHADAWEQOAGAsIgcAMBaRAwAYi8gBAIxF5AAAxiJyAABjETkAgLGIHADAWEQOAGAsIgcAMBaRAwAYi8gBAIxF5AAAxiJyAABjETkAgLF8Grn09HRNnDjRl4cEAOAX40wOAGCsJh25Q4cO2T0CACCA+TxybrdbDz74oOLi4tS2bVvl5eV5bvvpp590xx13qFWrVoqJidEVV1yhkpISz+15eXnq3r27nnvuOZ177rkKDw9v0P0AADgZn0du4cKFioqKUlFRkWbMmKGpU6fq3XfflSRlZWXp+++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    {
     "data": {
      "text/plain": [
       "'this is my life .'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load checkpoints\n",
    "model = Sequence2Sequence(len(src_word2idx), len(trg_word2idx))\n",
    "model.load_state_dict(torch.load(f\"checkpoints/{exp_name}/best.ckpt\", map_location=\"cpu\"))\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        \n",
    "    def draw_attention_map(self, scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        \n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        plt.matshow(scores.T, cmap='viridis')\n",
    "        plt.xticks(range(scores.shape[0]), src_words_list)\n",
    "        plt.yticks(range(scores.shape[1]), trg_words_list)\n",
    "        plt.show()\n",
    "        \n",
    "    def __call__(self, sentence):\n",
    "        sentence = preprocess_sentence(sentence)\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            [sentence.split()], \n",
    "            padding_first=True, \n",
    "            add_bos=True, \n",
    "            add_eos=True, \n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "\n",
    "        preds, scores = model.infer(encoder_input=encoder_input, attn_mask=attn_mask) #预测\n",
    "        \n",
    "        trg_sentence = self.trg_tokenizer.decode([preds], split=True, remove_eos=False)[0] #通过tokenizer转换成文字\n",
    "        \n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.tolist(), \n",
    "            split=True, \n",
    "            remove_bos=False, \n",
    "            remove_eos=False\n",
    "            )[0] #对输入编码id进行解码，转换成文字\n",
    "        \n",
    "        self.draw_attention_map(\n",
    "            scores.squeeze(0).numpy(), \n",
    "            src_decoded,\n",
    "            trg_sentence\n",
    "            )\n",
    "        return \" \".join(trg_sentence[:-1])\n",
    "        \n",
    "    \n",
    "translator = Translator(model.cpu(), src_tokenizer, trg_tokenizer)\n",
    "translator(u'hace mucho frio aqui .')\n",
    "translator(u'esta es mi vida.')    "
   ]
  }
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